Situational Awareness in Distribution Grid Using Micro-PMU Data: A Machine Learning Approach

Shahsavari, Alireza; Farajollahi, Mohammad; Stewart, Emma M.; Cortez, Ed; Mohsenian-Rad, Hamed

doi:10.1109/tsg.2019.2898676

Title: Situational Awareness in Distribution Grid Using Micro-PMU Data: A Machine Learning Approach

Abstract

The recent development of distribution-level phasor measurement units, a.k.a. micro-PMUs, has been an important step towards achieving situational awareness in power distribution networks. The challenge however is to transform the large amount of data that is generated by micro-PMUs to actionable information and then match the information to use cases with practical value to system operators. This open problem is addressed in this paper. First, we introduce a novel data-driven event detection technique to pick out valuable portion of data from extremely large raw micro-PMU data. Subsequently, a datadriven event classifier is developed to effectively classify power quality events. Importantly, we use field expert knowledge and utility records to conduct an extensive data-driven event labeling. Moreover, certain aspects from event detection analysis are adopted as additional features to be fed into the classifier model. In this regard, a multi-class support vector machine (multi-SVM) classifier is trained and tested over 15 days of real-world data from two micro-PMUs on a distribution feeder in Riverside, CA. In total, we analyze 1.2 billion measurement points, and 10,700 events. Here, the effectiveness of the developed event classifier is compared with prevalent multi-class classification methods, including k-nearest neighbor method as well as decision-tree method. Importantly,more »« less

Authors:

^[1];

^[1]; Stewart, Emma M. ^[2]; Cortez, Ed ^[3];

^[1]

Univ. of California, Riverside, CA (United States)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Riverside Public Utilities, CA (United States)

Publication Date:: Tue Feb 12 00:00:00 EST 2019

Research Org.:: Univ. of California, Riverside, CA (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE); National Aeronautics and Space Administration (NASA)

OSTI Identifier:: 1811286

Grant/Contract Number:: EE0008001; NNX15AP99A

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Smart Grid

Additional Journal Information:: Journal Volume: 10; Journal Issue: 6; Journal ID: ISSN 1949-3053

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 24 POWER TRANSMISSION AND DISTRIBUTION; Machine learning; distribution synchrophasors; situational awareness; event detection; event classification; big-data

Citation Formats


                    Shahsavari, Alireza, Farajollahi, Mohammad, Stewart, Emma M., Cortez, Ed, and Mohsenian-Rad, Hamed. Situational Awareness in Distribution Grid Using Micro-PMU Data: A Machine Learning Approach.  United States: N. p., 2019. 
Web.  doi:10.1109/tsg.2019.2898676.

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                    Shahsavari, Alireza, Farajollahi, Mohammad, Stewart, Emma M., Cortez, Ed, & Mohsenian-Rad, Hamed. Situational Awareness in Distribution Grid Using Micro-PMU Data: A Machine Learning Approach.  United States.  https://doi.org/10.1109/tsg.2019.2898676

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                    Shahsavari, Alireza, Farajollahi, Mohammad, Stewart, Emma M., Cortez, Ed, and Mohsenian-Rad, Hamed. Tue .  
"Situational Awareness in Distribution Grid Using Micro-PMU Data: A Machine Learning Approach".  United States.  https://doi.org/10.1109/tsg.2019.2898676.  https://www.osti.gov/servlets/purl/1811286.

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@article{osti_1811286,

  title        = {Situational Awareness in Distribution Grid Using Micro-PMU Data: A Machine Learning Approach},

  author       = {Shahsavari, Alireza and Farajollahi, Mohammad and Stewart, Emma M. and Cortez, Ed and Mohsenian-Rad, Hamed},

  abstractNote = {The recent development of distribution-level phasor measurement units, a.k.a. micro-PMUs, has been an important step towards achieving situational awareness in power distribution networks. The challenge however is to transform the large amount of data that is generated by micro-PMUs to actionable information and then match the information to use cases with practical value to system operators. This open problem is addressed in this paper. First, we introduce a novel data-driven event detection technique to pick out valuable portion of data from extremely large raw micro-PMU data. Subsequently, a datadriven event classifier is developed to effectively classify power quality events. Importantly, we use field expert knowledge and utility records to conduct an extensive data-driven event labeling. Moreover, certain aspects from event detection analysis are adopted as additional features to be fed into the classifier model. In this regard, a multi-class support vector machine (multi-SVM) classifier is trained and tested over 15 days of real-world data from two micro-PMUs on a distribution feeder in Riverside, CA. In total, we analyze 1.2 billion measurement points, and 10,700 events. Here, the effectiveness of the developed event classifier is compared with prevalent multi-class classification methods, including k-nearest neighbor method as well as decision-tree method. Importantly, two real-world use-cases are presented for the proposed data analytics tools, including remote asset monitoring and distribution-level oscillation analysis.},

  doi          = {10.1109/tsg.2019.2898676},

  journal      = {IEEE Transactions on Smart Grid},

  number       = 6,

  volume       = 10,

  place        = {United States},

  year         = {Tue Feb 12 00:00:00 EST 2019},

  month        = {Tue Feb 12 00:00:00 EST 2019}

}

Copy to clipboard

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Works referenced in this record:

Anomaly Detection Using Optimally Placed $\mu \text{PMU}$ Sensors in Distribution Grids
journal, July 2018

Jamei, Mahdi; Scaglione, Anna; Roberts, Ciaran
IEEE Transactions on Power Systems, Vol. 33, Issue 4
DOI: 10.1109/TPWRS.2017.2764882

Nonparametric Event Detection in Multiple Time Series for Power Distribution Networks
journal, February 2019

Zhou, Yuxun; Arghandeh, Reza; Zou, Han
IEEE Transactions on Industrial Electronics, Vol. 66, Issue 2
DOI: 10.1109/TIE.2018.2840508

Abnormal event detection with high resolution micro-PMU data
conference, June 2016

Zhou, Yuxun; Arghandeh, Reza; Konstantakopoulos, Ioannis
2016 Power Systems Computation Conference (PSCC)
DOI: 10.1109/PSCC.2016.7540980

Islanding and Power Quality Disturbance Detection in Grid-Connected Hybrid Power System Using Wavelet and $S$-Transform
journal, September 2012

Ray, Prakash K.; Kishor, Nand; Mohanty, Soumya R.
IEEE Transactions on Smart Grid, Vol. 3, Issue 3
DOI: 10.1109/TSG.2012.2197642

A Classification Approach for Power Distribution Systems Fault Cause Identification
journal, February 2006

Xu, L.; Chow, M. -Y.
IEEE Transactions on Power Systems, Vol. 21, Issue 1
DOI: 10.1109/TPWRS.2005.861981

A study of power distribution system fault classification with machine learning techniques
conference, October 2015

Coleman, Nicholas S.; Schegan, Christian; Miu, Karen N.
2015 North American Power Symposium (NAPS)
DOI: 10.1109/NAPS.2015.7335264

An ANFIS-based fault classification approach in power distribution system
journal, July 2013

Zhang, J.; He, Z. Y.; Lin, S.
International Journal of Electrical Power & Energy Systems, Vol. 49
DOI: 10.1016/j.ijepes.2012.12.005

PQ Monitoring System for Real-Time Detection and Classification of Disturbances in a Single-Phase Power System
journal, August 2008

Radil, Tomas; Ramos, Pedro M.; Janeiro, Fernando M.
IEEE Transactions on Instrumentation and Measurement, Vol. 57, Issue 8
DOI: 10.1109/TIM.2008.925345

Detection and Classification of Power Quality Disturbances Using S-Transform and Probabilistic Neural Network
journal, January 2008

Mishra, S.; Bhende, C. N.; Panigrahi, B. K.
IEEE Transactions on Power Delivery, Vol. 23, Issue 1
DOI: 10.1109/TPWRD.2007.911125

A PMU-data-driven disruptive event classification in distribution systems
journal, April 2018

Niazazari, Iman; Livani, Hanif
Electric Power Systems Research, Vol. 157
DOI: 10.1016/j.epsr.2017.12.021

Location identification of distribution network events using synchrophasor data
conference, September 2017

Farajollahi, Mohammad; Shahsavari, Alireza; Mohsenian-Rad, Hamed
2017 North American Power Symposium (NAPS)
DOI: 10.1109/NAPS.2017.8107272

Locating the Source of Events in Power Distribution Systems Using Micro-PMU Data
journal, November 2018

Farajollahi, Mohammad; Shahsavari, Alireza; Stewart, Emma M.
IEEE Transactions on Power Systems, Vol. 33, Issue 6
DOI: 10.1109/TPWRS.2018.2832126

A systematic analysis of performance measures for classification tasks
journal, July 2009

Sokolova, Marina; Lapalme, Guy
Information Processing & Management, Vol. 45, Issue 4
DOI: 10.1016/j.ipm.2009.03.002

Distribution Grid Reliability Versus Regulation Market Efficiency: An Analysis Based on Micro-PMU Data
journal, November 2017

Shahsavari, Alireza; Sadeghi-Mobarakeh, Ashkan; Stewart, Emma M.
IEEE Transactions on Smart Grid, Vol. 8, Issue 6
DOI: 10.1109/TSG.2017.2718560

Autopsy on active distribution networks: A data-driven fault analysis using micro-PMU data
conference, September 2017

Shahsavari, Alireza; Farajollahi, Mohammad; Stewart, Emma
2017 North American Power Symposium (NAPS)
DOI: 10.1109/NAPS.2017.8107265

A data-driven analysis of capacitor bank operation at a distribution feeder using micro-PMU data
conference, April 2017

Shahsavari, Alireza; Farajollahi, Mohammad; Stewart, Emma
2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)
DOI: 10.1109/ISGT.2017.8085984

Event detection and localization in distribution grids with phasor measurement units
conference, July 2017

Ardakanian, Omid; Yuan, Ye; Dobbe, Roel
2017 IEEE Power & Energy Society General Meeting (PESGM)
DOI: 10.1109/PESGM.2017.8273895

A data-driven analysis of lightning-initiated contingencies at a distribution grid with a PV farm using Micro-PMU data
conference, September 2017

Shahsavari, Alireza; Farajollahi, Mohammad; Stewart, Emma
2017 North American Power Symposium (NAPS)
DOI: 10.1109/NAPS.2017.8107307

Distribution Synchrophasors: Pairing Big Data with Analytics to Create Actionable Information
journal, May 2018

Mohsenian-Rad, Hamed; Stewart, Emma; Cortez, Ed
IEEE Power and Energy Magazine, Vol. 16, Issue 3
DOI: 10.1109/MPE.2018.2790818

Partial Knowledge Data-Driven Event Detection for Power Distribution Networks
journal, September 2018

Zhou, Yuxun; Arghandeh, Reza; Spanos, Costas J.
IEEE Transactions on Smart Grid, Vol. 9, Issue 5
DOI: 10.1109/TSG.2017.2681962

A comparison of methods for multiclass support vector machines
journal, March 2002

Hsu, Chih-Wei; Lin, Chih-Jen
IEEE Transactions on Neural Networks, Vol. 13, Issue 2, p. 415-425
DOI: 10.1109/72.991427

Field Experiments on 10 kV Switching Shunt Capacitor Banks Using Ordinary and Phase-Controlled Vacuum Circuit Breakers
journal, January 2016

Sima, Wenxia; Zou, Mi; Yang, Qing
Energies, Vol. 9, Issue 2
DOI: 10.3390/en9020088

Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid
journal, June 2016

Jindal, Anish; Dua, Amit; Kaur, Kuljeet
IEEE Transactions on Industrial Informatics, Vol. 12, Issue 3
DOI: 10.1109/TII.2016.2543145

A Proposal for Local $k$ Values for $k$ -Nearest Neighbor Rule
journal, February 2017

Garcia-Pedrajas, Nicolas; Romero del Castillo, Juan A.; Cerruela-Garcia, Gonzalo
IEEE Transactions on Neural Networks and Learning Systems, Vol. 28, Issue 2
DOI: 10.1109/TNNLS.2015.2506821

Works referencing / citing this record:

Event Detection in Micro-PMU Data: A Generative Adversarial Network Scoring Method
text, January 2019

Aligholian, Armin; Shahsavari, Alireza; Cortez, Ed
arXiv
DOI: 10.48550/arxiv.1912.05103

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Similar Records

Title: Situational Awareness in Distribution Grid Using Micro-PMU Data: A Machine Learning Approach

Abstract

Citation Formats

Anomaly Detection Using Optimally Placed $\mu \text{PMU}$ Sensors in Distribution Grids journal, July 2018

Nonparametric Event Detection in Multiple Time Series for Power Distribution Networks journal, February 2019

Abnormal event detection with high resolution micro-PMU data conference, June 2016

Islanding and Power Quality Disturbance Detection in Grid-Connected Hybrid Power System Using Wavelet and $S$-Transform journal, September 2012

A Classification Approach for Power Distribution Systems Fault Cause Identification journal, February 2006

A study of power distribution system fault classification with machine learning techniques conference, October 2015

An ANFIS-based fault classification approach in power distribution system journal, July 2013

PQ Monitoring System for Real-Time Detection and Classification of Disturbances in a Single-Phase Power System journal, August 2008

Detection and Classification of Power Quality Disturbances Using S-Transform and Probabilistic Neural Network journal, January 2008

A PMU-data-driven disruptive event classification in distribution systems journal, April 2018

Location identification of distribution network events using synchrophasor data conference, September 2017

Locating the Source of Events in Power Distribution Systems Using Micro-PMU Data journal, November 2018

A systematic analysis of performance measures for classification tasks journal, July 2009

Distribution Grid Reliability Versus Regulation Market Efficiency: An Analysis Based on Micro-PMU Data journal, November 2017

Autopsy on active distribution networks: A data-driven fault analysis using micro-PMU data conference, September 2017

A data-driven analysis of capacitor bank operation at a distribution feeder using micro-PMU data conference, April 2017

Event detection and localization in distribution grids with phasor measurement units conference, July 2017

A data-driven analysis of lightning-initiated contingencies at a distribution grid with a PV farm using Micro-PMU data conference, September 2017

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Partial Knowledge Data-Driven Event Detection for Power Distribution Networks journal, September 2018

A comparison of methods for multiclass support vector machines journal, March 2002

Field Experiments on 10 kV Switching Shunt Capacitor Banks Using Ordinary and Phase-Controlled Vacuum Circuit Breakers journal, January 2016

Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid journal, June 2016

A Proposal for Local $k$ Values for $k$ -Nearest Neighbor Rule journal, February 2017

Event Detection in Micro-PMU Data: A Generative Adversarial Network Scoring Method text, January 2019

Anomaly Detection Using Optimally Placed $\mu \text{PMU}$ Sensors in Distribution Grids
journal, July 2018

Nonparametric Event Detection in Multiple Time Series for Power Distribution Networks
journal, February 2019

Abnormal event detection with high resolution micro-PMU data
conference, June 2016

Islanding and Power Quality Disturbance Detection in Grid-Connected Hybrid Power System Using Wavelet and $S$-Transform
journal, September 2012

A Classification Approach for Power Distribution Systems Fault Cause Identification
journal, February 2006

A study of power distribution system fault classification with machine learning techniques
conference, October 2015

An ANFIS-based fault classification approach in power distribution system
journal, July 2013

PQ Monitoring System for Real-Time Detection and Classification of Disturbances in a Single-Phase Power System
journal, August 2008

Detection and Classification of Power Quality Disturbances Using S-Transform and Probabilistic Neural Network
journal, January 2008

A PMU-data-driven disruptive event classification in distribution systems
journal, April 2018

Location identification of distribution network events using synchrophasor data
conference, September 2017

Locating the Source of Events in Power Distribution Systems Using Micro-PMU Data
journal, November 2018

A systematic analysis of performance measures for classification tasks
journal, July 2009

Distribution Grid Reliability Versus Regulation Market Efficiency: An Analysis Based on Micro-PMU Data
journal, November 2017

Autopsy on active distribution networks: A data-driven fault analysis using micro-PMU data
conference, September 2017

A data-driven analysis of capacitor bank operation at a distribution feeder using micro-PMU data
conference, April 2017

Event detection and localization in distribution grids with phasor measurement units
conference, July 2017

A data-driven analysis of lightning-initiated contingencies at a distribution grid with a PV farm using Micro-PMU data
conference, September 2017

Distribution Synchrophasors: Pairing Big Data with Analytics to Create Actionable Information
journal, May 2018

Partial Knowledge Data-Driven Event Detection for Power Distribution Networks
journal, September 2018

A comparison of methods for multiclass support vector machines
journal, March 2002

Field Experiments on 10 kV Switching Shunt Capacitor Banks Using Ordinary and Phase-Controlled Vacuum Circuit Breakers
journal, January 2016

Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid
journal, June 2016

A Proposal for Local $k$ Values for $k$ -Nearest Neighbor Rule
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